Teaching

Using spinImages for 3D object classification

The subject of this thesis is to evaluate if a combination of spin images and Bag of Features classification can be used to categorize point clouds representing real world objects. At first, for each point cloud spin images are created as descriptors for the cloud's points. Then vector quantization
is used to learn prototypes in the spin image space. These prototypes are then used as a codebook for extracting features for each point cloud in the training set, which means, that standard bag of features approach is used. Afterwards these features are used to train a support vector machine. Using test sets disjunct from the training set, best parameters for the SVM as well as for spin image generation will be computed. Two different datasets will be used, to evaluate the approach. One of these consists of handmade 3d models as found in a subset of the Princeton Shape Benchmark and one consists of 3D scans obtained by using a range scanner on real objects. Both used datasets consist of models which represent furniture of different kinds.